Author:
Sharma Ravi,Mohi ud din Saika,Sharma Nonita,Kumar Arun
Abstract
An increase in cyberattacks has coincided with the Internet of Things (IoT) expansion. When numerous systems are connected, more botnet attacks are possible. Because botnet attacks are constantly evolving to take advantage of security holes and weaknesses in internet traffic and IoT devices, they must be recognized. Voting ensemble (VE), Ada boost, K-Nearest Neighbour (KNN), and bootstrap aggregation are some methods used in this work for botnet detection. This study aims to first incorporate feature significance for enhanced efficacy, then estimate effectiveness in IoT botnet detection using traditional model-based machine learning, and finally evaluate the outcomes using ensemble models. It has been demonstrated that applying feature importance increases the effectiveness of ensemble models. VE algorithm provides the best botnet traffic detection compared to all currently used approaches.
Publisher
European Alliance for Innovation n.o.
Subject
Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Software
Reference19 articles.
1. M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, "Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches," Internet of Things, vol. 7, p. 100059, Sep. 2019, doi: 10.1016/J.IOT.2019.100059.
2. A. Shahid, M. Z. Jasni, Z. Mohamad Fadli, and I. Zakira, "A Review Paper on Botnet and Botnet Detection Techniques in Cloud Computing," 2014, Accessed: May 03, 2023. [Online]. Available: https://www.researchgate.net/profile/Shahid_Anwar3/publication/283257776_A_Review_Paper_on_Botnet_and_Botnet_Detection_Techniques_in_Cloud_Computing/links/562f525308ae4742240abea7.pdf
3. SharmaRavi and SharmaNonita, "Attacks on Resource-Constrained IoT Devices and Security Solutions," International Journal of Software Science and Computational Intelligence (IJSSCI), vol. 14, no. 1, pp. 1–21, Oct. 2022, doi: 10.4018/IJSSCI.310943.
4. X. Liu, Y. Liu, A. Liu, and L. T. Yang, "Defending ON-OFF attacks using light probing messages in smart sensors for industrial communication systems," IEEE Trans Industr Inform, vol. 14, no. 9, pp. 3801–3811, Sep. 2018, doi: 10.1109/TII.2018.2836150.
5. H. H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha, and K. K. R. Choo, "A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks," IEEE Trans Emerg Top Comput, vol. 7, no. 2, pp. 314–323, 2019, doi: 10.1109/TETC.2016.2633228.